JP2004318503A - Device, method and program for supporting action management - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an action management supporting device for conventionalizing exercise in a daily life and naturally increasing the amount of exercise, and also to provide its method, and its program. <P>SOLUTION: An individual attribute data aggregation 1, an action related data group 2, a sensor data aggregation 3, and a related data aggregation 9 are mutually related by a synthetic action data generating part 61, so as to generate a synthetically collected synthetic action data aggregation 7. An action rule generating part 62 generates the rule of the action of a user by referring to the synthetic action data aggregation 7, so that an action rule aggregation 8 is generated. An advice for promoting exercise is provided to the user by indicating an explicit reason, based on the exercise custom, characteristic, taste, and habit of each individual which are reflected on the generated action rule aggregation 8. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention obtains various states of a user with a wearable device worn and used by the user, and based on the acquired states, a behavior management support apparatus, a behavior management support method, and a behavior management support About the program.
[0002]
[Prior art]
As an apparatus to which this type of system is applied, there is an apparatus disclosed in Patent Document 1 below. According to the device described in this document, the user's calorie consumption, body temperature, basal body temperature, blood pressure, heart rate, stress level, blood sugar level, urine sugar level, urine protein, sleep depth, body fat percentage, or body size, etc. Daily biological information is acquired. Then, the physiological biorhythm of the individual is determined from such information, and the user is notified together with the acquired biological information, whereby the individual can recognize the cause of the quality of the biological condition. According to this device, the user can be encouraged to exercise at an optimal timing, and for example, an application such as instructing a diet on a day when it is easy to lose weight can be made.
[0003]
However, this device does not take into account the daily behavior rules of each individual, and it is therefore difficult to explicitly indicate the reason for promoting exercise to the user. Further, it is difficult to flexibly generate an exercise schedule in daily life at a time level, and it is inevitable that the habit of exercising largely depends on the individual's will.
[0004]
On the other hand, according to the device disclosed in Patent Literature 2, in addition to the amount of exercise consumed in normal life, the user is instructed on how much exercise can be maintained to maintain the health of the user. It can be realized by exercise without lack. In other words, it is possible to calculate not only sports but also calories consumed in daily life, including the calories consumed in daily life, and the amount of exercise required to achieve the goal taking into account personal information such as age, gender, body type etc. Is indicated.
[0005]
However, with this device, only information about how much exercise is needed to reach the target value based on the registered target heart rate is presented, and the habit of exercising also depends on the individual's will. I have to say that it is big.
[0006]
That is, in order to increase the user's motivation and make the exercise a habit, it is necessary to provide appropriate advice in more detail, and the timing of giving an instruction is also important. For that purpose, it is effective to generate a user's daily behavior rule and use it as one element, but such an apparatus is not yet known.
As a method for generating an individual behavior rule, a method described in Non-Patent Document 1 below is known. This document describes a method of generating an action rule of an individual in each time zone by dividing time into fixed lengths.
[0007]
[Patent Document 1]
JP-A-09-103413 (for example, paragraph numbers [0060] to [0063], FIGS. 15 to 17 and the like)
[0008]
[Patent Document 2]
JP-A-10-118052 (for example, paragraph numbers [0040] to [0051], FIG. 5, etc.)
[0009]
[Non-patent document 1]
GSP (R. Srikant, R. Agrawal., "Minning Sequential Patterns: Generalizations and Performance Improvements", Proc. 5th Int. J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, Mei-Chun Hsu, "PrefixSpan: Mining Sequential PatternsProfessional Graphic Principle proof proof proof proof proof proof proof proof prize proof proof proof proof proof proof proof proof proof proofing. ternational Conference of Data Engineering (ICDE2001), pp.215-224, 2001)
[0010]
[Problems to be solved by the invention]
As described above, depending on the existing technology, it is largely dependent on the will of the user to increase the amount of exercise of the user, and in particular, it is desired that the user's burden can be further reduced in order to habitually exercise.
[0011]
The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a behavior management support apparatus, a behavior management support method, and a behavior management support program that can make a habit of exercising in daily life and increase the amount of exercise without difficulty. To provide.
[0012]
[Means for Solving the Problems]
In order to achieve the above object, the present invention provides a behavior management support device for managing the behavior of a user using biometric information of the user detected by a sensor worn on the body of the user, Integrated action database generating means for generating an integrated action database in which biological information detected by the sensor and action-related information related to the action of the user are associated with each other in a time-series manner; A behavior rule generating means for generating the user's behavior rule from the integrated behavior database, and a message for prompting the user to exercise based on the user's behavior rule generated by the behavior rule generation means. Message generating means for generating, and a message notifying the user of the message generated by the message generating means. Characterized by comprising a chromatography di notifying means.
[0013]
By taking such measures, the biometric information and the behavioral information of the user are integrated into a database from the biometric data, event data, schedule data, etc. relating to exercise, and the behavior rules of the user are generated from this. You. This behavior rule reflects the user's motor behavior characteristics, habits, preferences, habits, and the like. The message generated by the message generating means based on this behavior rule explicitly reflects the behavior rule in consideration of the personal characteristics of the user in daily life, and is used by giving this message. Can easily increase their motivation. Therefore, it becomes possible for the user to habitually increase the amount of exercise in daily life.
[0014]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
[0015]
FIG. 1 is a diagram schematically illustrating a configuration example of a system including a behavior management support device according to the present invention. This system is mainly composed of a behavior management support device (hereinafter, referred to as a main unit C1) carried by a user (hereinafter, referred to as a user). Take the form. The main unit C1 is realized as a portable terminal such as a PDA (Personal Digital Assistants), a mobile phone, a PHS, a wristwatch, or the like. The main unit C1 and the server device C2 are connected via an information communication network such as the Internet, and exchange necessary information with each other. The interface between the main unit C1 and the server device C2 may be either wireless or wired.
[0016]
A sensor head C3 is connected to the main body C1 via a wired line or a wireless line such as Bluetooth (registered trademark). A sensor group C5 including, for example, a pedometer, a skin thermometer, and a pulse sensor is connected to the sensor head C3, and the sensor head C3 collects user's biological information acquired by the sensor group C5 and transmits the information to the main body C1. I do. A data input interface C4 is connected to the main body C1. The data input interface C4 is realized as a keyboard, a tablet, a voice input interface, or the like.
[0017]
FIG. 2 is a functional block diagram showing a first embodiment of the system shown in FIG. This system includes a behavior-related data group 2 for storing data relating to a user's behavior, and a behavior data processing unit 6 for acquiring and processing various data via a data input unit 4 and a data acquisition unit 5. In addition, this system includes a personal attribute data set 1, a sensor data set 3, an integrated action data set 7, an action rule set 8, a related data set 9, an action advisory set 10, an advisory evaluation set 11, And a motion constraint condition rule set 12. Each of these data sets is stored in a predetermined database.
[0018]
The action-related data group 2 includes an action data set 21, a mood description data set 22, and an action schedule data set 23, and these data sets are all stored in a predetermined database.
The action data processing section 6 includes an integrated action data generation section 61, an action rule generation section 62, an action schedule reorganization section 63, an action advice generation section 64, and an advice evaluation input section 65.
[0019]
The integrated behavior data generation unit 61 acquires the personal attribute data set 1, the behavior-related data group 2, and the related data set 9 via the data input unit 4, and acquires the sensor data set via the data acquisition unit 5. The integrated behavior data generation unit 61 associates the acquired data with each other in chronological order, and generates the integrated behavior data set 7.
[0020]
The behavior rule generation unit 62 generates a behavior rule of the user from the integrated behavior data set 7 by, for example, a method obtained by improving the method described in Non-Patent Document 1 for the present apparatus, and generates a behavior rule set 8. The action schedule reorganizing unit 63 adjusts the amount of exercise of the user with reference to the action rule set 8 and reorganizes the action schedule data set 23 to encourage more efficient exercise.
[0021]
The action advice generating unit 64 generates a message for encouraging the user to exercise from the action schedule data set 23, the action rule set 8, and the related data set 9 reorganized by the action schedule reorganization unit 63. The generated message is displayed on a liquid crystal display (not shown) or the like of the main unit C1, and is notified to the user.
[0022]
The advice evaluation input unit 65 acquires the user's evaluation of the notified message via the data input interface C4, and generates the advice evaluation set 11. The advice evaluation input unit 65 integrates the evaluation of the user with respect to the message and the result of the user's action with respect to the message, and accumulates the result in the exercise constraint condition rule set 12. The exercise constraint condition rule set 12 is reused as input data to the behavioral schedule reorganization unit 63 and the behavioral advice generation unit 64.
[0023]
FIG. 3 is a diagram illustrating an example of the content recorded in the personal attribute data set 1. The personal attribute data set 1 is a database in which data such as user names, names, ages, genders, occupations, addresses, workplaces, and passwords are associated with one another.
[0024]
FIG. 4 is a diagram showing an example of the content recorded in the behavior data set 21. The action data set 21 is a database in which a date, a start time, an end time, a current point (FROM), a destination point (TO), a user name, an action label, and a route followed by the user are associated with each other.
[0025]
FIG. 5 is a diagram showing an example of the content recorded in the mood description data set 22. The mood description data set 22 is a database in which date, start time, end time, user name, mood, and mood description data input by the user are associated with each other.
[0026]
FIG. 6 is a diagram showing an example of the content recorded in the action schedule data set 23. The action schedule data set 23 is created in advance based on the user's intention, and includes a date, a start time, an end time, a current point (FROM), a destination point (TO), a user name, an action label, and a schedule for the user to follow. This is a database that associates scheduled routes with each other.
[0027]
FIG. 7 is a diagram illustrating an example of the content recorded in the sensor data set 3. The sensor data set 3 is a database in which the date, the acquisition start time and the end time of the biological information by the sensor group C5, the sensor measurement value FROM at the source, and the sensor measurement TO at the destination are associated with each other.
[0028]
FIG. 8 is a diagram illustrating an example of the content recorded in the integrated behavior data set 7. The integrated behavior data set 7 is a database in which date, start time, end time, route, user name, behavior label, required time, start delay time, required extended time, number of steps, cumulative number of steps, mood, and mood description are associated with each other. . The integrated action data set 7 is generated by associating the contents recorded in the personal attribute data set 1, the action data set 21, the mood description data set 22, the action schedule data set 23, and the sensor data set 3 with one another.
[0029]
FIG. 9 is a diagram illustrating an example of the content recorded in the behavior rule set 8. The behavior rule set 8 is a database in which the user's behavior rules generated by the behavior rule generation unit 62 from the integrated behavior data set 7 are stored in a database. It indicates that there is a tendency to go shopping every other day (except on rainy days). In addition, it is shown that the number of steps is increased by going to shopping compared to a normal day. However, increasing the number of steps does not make much sense if the work efficiency is poor. Therefore, write the work efficiency in the mood description.
[0030]
FIG. 10 is a diagram illustrating an example of the content recorded in the related data set 9. FIG. 10 shows a concept dictionary data set as an example of the contents of the related data set 9. The concept dictionary data set includes four items: a superordinate concept, a subordinate concept, a symbolic expression, and a condition. For example, a superordinate concept of a break has two subordinate concepts of a meal and a break in the afternoon. The subordinate concept of meal is expressed as lunch or dinner as a representational expression. By using this concept dictionary data set to determine the level of various concepts to be summarized, it is possible to operate the delimiter and the way of grouping the numerical data attached to the action label and the text information.
[0031]
FIG. 11 is a diagram illustrating an example of the content recorded in the related data set 9. FIG. 11 shows an action label set as an example of the content of the related data set 9. The action label set includes a starting point (FROM), an arrival point (TO), a condition, and an action label. The name of the action is determined by determining the starting point and the arriving point. By referring to the action label set using the departure point (FROM) and the arrival point (TO) as keywords, the label of the action can be specified. If there is an additional condition for each action label, add it to the condition column.
[0032]
FIG. 12 is a diagram illustrating an example of the content recorded in the related data set 9. FIG. 12 shows a calendar weather data set as an example of the contents of the related data set 9. The calendar weather data set associates each date with the day of the week, whether it is a holiday, whether it is a holiday, whether it is an annual holiday, what week it is, the weather for each day and night, the average temperature, and the average humidity. Database. These data are collected from other databases or separately recorded values measured by sensors.
[0033]
FIG. 13 is a diagram illustrating an example of the content recorded in the related data set 9. FIG. 13 shows a root data set as an example of the contents of the related data set 9. The route data set is a database in which map data, a starting point (FROM), an arrival point (TO), a route, and a point list are associated with a route label (name of a route).
[0034]
FIG. 14 is a diagram illustrating an example of the content recorded in the related data set 9. FIG. 14 shows a location data set as an example of the contents of the related data set 9. The location data set is a database in which map information recorded in a format such as a bit map is associated with a point, a name label, and a location. By using this database, it is possible to recognize where each location is located on which map and on which map.
[0035]
FIG. 15 is a diagram illustrating an example of the content recorded in the related data set 9. FIG. 15 shows a map data set as an example of the contents of the related data set 9. The map data set is data in which the map information included in the location data set is associated with a point, a route, and the like. By referring to this information, the positional relationship between the point and the route, and the position of each point in the map data, It is possible to know the information about whether there is.
[0036]
FIG. 16 is a diagram illustrating an example of the content recorded in the related data set 9. FIG. 16 shows a map-related data set as an example of the contents of the related data set 9. The map relation data set is a data set representing the relation between the maps, and by referring to this data, it is possible to know the relation of where the data corresponding to the detailed map of a certain part of a certain map is. The related data set 9 described above is appropriately used in each phase of the processing procedure described later.
[0037]
FIG. 17 is a diagram illustrating an example of the content recorded in the action advice set 10. The action advice set 10 is a database that associates the advice (that is, a message) given to the user with the expected total number of steps. The expected total number of steps is a predicted value calculated from the predicted value of the step count data history for each past action based on the action schedule data set 23. To obtain the predicted value, an averaging process may be used, or a regression analysis result or a value of a clustering centroid may be used.
[0038]
FIG. 18 is a diagram illustrating an example of the content recorded in the motion constraint condition rule set 12. The exercise constraint condition rule set 12 is a database in which the evaluation of the message given by the user and the content recorded in the action advice set 10 are associated with each other. By referring to this database, the system can generate a flexible message tailored to each user's behavioral preference characteristics.
[0039]
Next, the operation in the above configuration will be described by taking daily step count management as an example.
FIG. 19 is a flowchart illustrating an example of a processing procedure in the above configuration. In this flowchart, <use preparation phase>, <setting phase>, <monitoring phase>, <behavior description phase>, <behavior rule generation phase>, <scheduling phase>, <advising phase>, and <feedback phase> The description is divided into eight phases.
[0040]
<Preparation phase>
First, prior to the start of the main program, the main unit C1, the sensor head C3, and the sensor group C5 are fixed to an appropriate place on the user's body with a belt or a clip. The sensor head C3 and the sensor group C5 are mounted at appropriate places according to their types. When this device is used as a pedometer, it is preferable to mount the sensor head C3 and the sensor group C5 around the waist. Then, when the user turns on the main unit C1, the program starts, and first, a check is made to see if the sensor is working properly and a calibration is performed. After that, the main program is started.
[0041]
<Setting phase>
In FIG. 19, if this is the first time the user has used this program (Y in step S11), the user's personal attribute data shown in FIG. 3 is input via the keypad of the main body C1 (FIG. 19). Step S12). If it is not the first time (N in step S11), step S12 is skipped and the process proceeds to the next login step.
[0042]
In the login step, the user logs in to the system by inputting a user name and a password (step S13). Next, when the user wants to end the system (Y in step S14), the user logs out. If not logged out (N in step S14), otherwise proceed to the next step.
[0043]
Next, when there is action schedule data (Y in step S15), the action schedule data is input by reading the data from the action schedule data set 23, writing the data with a scheduler or an editor, or reading from another scheduler. After that (step S16), the processing shifts to the BDI program. If there is no action schedule data (N in step S15), the process skips step S16 and shifts to the BDI program.
[0044]
<Monitoring phase>
When the BDI program is started, first, when no action data is input (N in step S21), the process shifts to the DA program. In the DA program, sensor data is constantly sampled at a preset sampling rate, and the value is monitored (step S41). When the sensor data exceeds or falls below a specified value, or when an abnormal pattern is indicated, this is regarded as an abnormal value (Y in step S42) and a warning is given to the user (step S43). At this time, while always buffering the data (step S44), the data is accumulated as the sensor data set 3 (step S45).
[0045]
<Action description phase>
On the other hand, when the action data is input (Y in step S21), the action data is input first (step S22). In this step, for example, in order to input an action of “working in”, the procedure shown in FIGS. 20 to 25 is followed.
[0046]
20 to 25 are diagrams showing an outline of the action description process using the data input interface C4 of the main body C1. First, when the action recording mode button (displayed in FIG. 33) of the data input interface C4 is clicked, the state of the program shifts to the action recording mode. Next, when the two-point generation button is clicked, a circular point node appears on the screen (FIG. 20).
[0047]
Next, as shown in FIG. 21, each point node is specified by pressing, and the name of the place is defined using a keyboard or the like. In FIG. 21, names such as home and work are entered. In this procedure, a name option may be prepared in advance, and the name may be selected using a drop-down list or the like.
[0048]
Next, as shown in FIG. 22, when it is time for the user to move, the two-point move button is clicked. In this state, first, the current point node (home) is clicked, and then the destination point node (workplace) is clicked.
[0049]
Then, as shown in FIG. 23, the current time and the measured number of steps are displayed near the current position node. This value is recorded. Also, an arrow (arc) from the current location node to the destination location node is displayed. When the movement start button is clicked in this state, the program switches to the moving state.
[0050]
When the user arrives at the destination, the current position setting button is clicked as shown in FIG. 24, and the state where the user arrives at the destination is recorded by changing the current location. Then, the current arrival time and the measured number of steps at that time are displayed and recorded near the destination point node (workplace).
[0051]
A few seconds after this state, a summary of the behavior described by the above procedure is displayed on the arc. This data is also recorded. In FIG. 25, an action summary including an action label, a movement time, the number of steps, and a time zone is described on an arc connecting the two points. The action label is defined as a pair of a departure point and an arrival point, and is a name for specifying the action. The action label in FIG. 25 is “Work”.
[0052]
The action label is specified by referring to the action label set (FIG. 11). If the pair of the departure point and the arrival point is unknown, the user registers a new behavior label.
[0053]
When the mood of each action is described in the flowchart of FIG. 19 (step S23), a predetermined button of the main body C1 is clicked to display a text box, and the mood is described therein. And the action summary box, the user inputs mood data (step S24). By the above basic procedure, actions are added one after another. The behavior data obtained by this is recorded in the device.
[0054]
For example, when an action is added, such as when stopping at a place other than home when leaving the office, a waypoint is first added. That is, as shown in FIG. 26, the user clicks the via point addition button while moving between two point nodes.
[0055]
Then, a new point node is displayed on the arc, and the place name of the node is defined in the same manner as in FIG. When the vehicle arrives at this waypoint, the user clicks the current location setting button, and then clicks the waypoint node. Then, as shown in FIG. 30, the same action summary as in FIG. 25 is generated. Hereinafter, by clicking the movement start button (FIG. 29) and clicking the current location setting button when arriving (FIG. 30), the action is described in the same manner as described above.
[0056]
According to such a processing procedure, data as shown in FIGS. 4 and 5 are accumulated in the main body part C1 as the behavior data set 21 and the mood description data set 22.
[0057]
Now, after step S24 in the flowchart of FIG. 19, the processing procedure shifts to the BPM program. In this program, data sequentially stored in the sensor data set 3 is cut out based on the start time and the end time of the action data set 21 (step S31). In this way, the biological state and the surrounding state acquired from the sensor measurement values can be divided for each action (segmentation).
[0058]
Next, based on the action data set 21, the mood description data set 22, the action schedule data set 23, the sensor data set 3, and the related data set 9, the integrated action data set 7 shown in FIG. Is generated (step S32).
[0059]
FIG. 31 is a diagram schematically illustrating a process of generating the integrated behavior data set 7. As shown in this figure, the integrated behavioral data set 7 is generated by, for example, integrating the behavioral schedule data set 23, the behavioral data set 21, the mood description data set 22, and the sensor data set 3 in an integrated manner. The data of the related data set 9 is also referred to as needed.
[0060]
<Behavior rule generation phase>
Next, the behavior rule generation unit 62 filters the integrated behavior data set 7 based on a predetermined criterion, and generates the behavior rule set 8 of FIG. 9 by referring to the related data set 9 if necessary (step S33). ). Here, for example, the cut points such as (actions that constantly exceed the step count target value), (actions changed so that the number of steps is increased), and (actions in which the mood description data includes a good one) are used as a reference. Can be. For example, an action rule set 8 in FIG. 8 is generated from the integrated action data set 7 in FIG. By using the information obtained from the calendar weather data set in FIG. 12 in this phase, it is possible to know the difference in behavior depending on the weather around a certain day and the difference in behavior depending on the day of the week.
[0061]
<Scheduling phase>
Next, the action of the user is scheduled. First, the presence / absence of the motion constraint condition rule set 12 is checked. If there is no condition rule (N in step S34), the process proceeds to step S36. If there is a condition rule (Y in step S34), the action schedule data set 23 is reorganized while observing the motion constraint condition rule set 12 (step S36).
[0062]
FIG. 32 is a diagram schematically showing a process of reorganizing the action schedule data set 23. As shown in FIG. 32, the action schedule reorganization unit 63 refers to the action rule set 8, adjusts the amount of exercise in order to encourage more efficient exercise, and reorganizes the action schedule data set 23.
[0063]
<Advising phase>
Next, the action advice generation unit 64 generates the action advice set 10 based on the action rule set 8 (step S37), and displays the advice message on, for example, the display of the main body C1 to notify the user. Note that the advice message may be notified to the user by voice, mail, or the like.
[0064]
<Feedback phase>
Next, when the user is notified of the advice message (that is, advice) (Y in step S38), the user evaluates the advice (step S39). In this case, for example, A (good advice and executed), B (good advice but cannot be executed this time), C (not very good advice), and D (completely misplaced advice) Are evaluated in four stages consisting of A to D.
[0065]
By associating the given advice evaluation with the action advice set 10, a motion constraint condition rule set 12 is generated and stored in the database (step S310). By using the exercise constraint condition rule set 12, the system can generate a flexible advice tailored to the behavioral preference characteristics of each user. That is, the advice evaluation input unit 65 integrates the user's evaluation and the action result with respect to the advice, accumulates them in the exercise constraint condition rule set 12, and reuses them as inputs for the action schedule reorganization unit 63 and the action advice generation unit 64. To Then, these procedures are repeated until the user logs out (step S14). The data generated by the above procedure is finally put together as an action graph.
[0066]
FIG. 33 is a schematic diagram illustrating an example of an action graph. As described above, the user's daily activities are visually summarized by using the present apparatus. For this reason, the present apparatus can be used as a self-management tool such as behavior management and working time management.
[0067]
As described above, in the present embodiment, the individual attribute data set 1, the action-related data group 2, the sensor data set 3, and the related data set 9 are associated with each other by the integrated action data generation unit 61, and integrated integration is performed. An action data set 7 is generated. With reference to the integrated behavior data set 7, the behavior rule of the user is generated by the behavior rule generation unit 62, and the behavior rule set 8 is generated. Then, based on the exercise habits, characteristics, preferences, and habits of each individual reflected in the generated behavior rule set 8, advice for promoting exercise is provided to the user in a form showing an explicit reason. ing.
[0068]
By doing so, the system can generate a flexible message according to the behavior preference characteristics of each user. In addition, since exercises in daily life such as walking and shopping can be incorporated into short periods of time, the exercise plan reorganization unit 63 incorporates an exercise plan in a flexible manner in response to changes in the behavior schedule. Can be. Even the person who is busy and unable to exercise can be encouraged to increase the amount of exercise in a familiar place by measuring the amount of exercise in daily life by the action advice generation unit 64.
[0069]
That is, according to the present embodiment, the user can know his / her exercise pattern and behavior rules, and thereby can make a plan for exercising without difficulty. By utilizing this, it becomes possible to acquire a habit of exercising naturally and naturally in daily life, and it is possible to further contribute to health management.
[0070]
(Second embodiment)
Next, a second embodiment of the present invention will be described.
FIG. 34 is a functional block diagram showing a second embodiment of the behavior management support system according to the present invention. In FIG. 34, portions common to FIG. 2 are denoted by the same reference numerals, and only different portions will be described here.
[0071]
The system in FIG. 34 includes a knowledge sharing unit 13 in addition to the configuration in FIG. The knowledge sharing unit 13 provides a basis for sharing the behavior rule set 8, the related data set 9, the behavior advice set 10, the advice evaluation set 11, and the exercise constraint rule set 12 among a plurality of users.
[0072]
By sharing the database generated by the main unit C1 possessed for each user among a plurality of users in this manner, it becomes possible to share the tips for managing the exercise behavior. Therefore, according to the present embodiment, in addition to the effects obtained in the first embodiment, it becomes possible for users to help each other when they are in trouble or when information is insufficient.
[0073]
(Third embodiment)
Next, a third embodiment of the present invention will be described.
FIG. 35 is a functional block diagram showing a third embodiment of the behavior management support system according to the present invention. In FIG. 35, portions common to FIGS. 2 and 34 are denoted by the same reference numerals, and only different portions will be described here.
[0074]
The system shown in FIG. 35 includes an automatic position detecting unit 14, and obtains a change in the user's instantaneous position detected by the automatic position detecting unit 14 as one of sensor data, and records the change in the sensor data set 3. It was done. The automatic position detection unit 14 is realized as a position specifying unit using, for example, a GPS (Global Positioning System) or an electronic checkpoint installed on a running course, a wireless tag, an IC card, Bluetooth (registered trademark), or the like.
[0075]
In such a configuration, by acquiring the position data obtained by the automatic position detection unit 14 as the sensor data set 3, it is possible to save the user from inputting the current position. Therefore, according to the present embodiment, in addition to the effects obtained in the first embodiment, the user's labor can be further reduced, and motivation can be further increased to continuously carry out exercise behavior.
[0076]
Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying constituent elements in an implementation stage without departing from the scope of the invention. Various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the above embodiments. For example, some components may be deleted from all the components shown in the embodiment. Further, components of different embodiments may be appropriately combined.
[0077]
【The invention's effect】
As described in detail above, according to the present invention, it is possible to provide a behavior management support device, a behavior management support method, and a behavior management support program that can make exercise a habit in daily life and increase the amount of exercise without difficulty. Can be.
[Brief description of the drawings]
FIG. 1 is a diagram schematically illustrating a configuration example of a system including a behavior management support device according to the present invention.
FIG. 2 is a functional block diagram showing a first embodiment of the system shown in FIG. 1;
FIG. 3 is a view showing an example of contents recorded in a personal attribute data set 1;
FIG. 4 is a diagram showing an example of contents recorded in an action data set 21.
FIG. 5 is a diagram showing an example of contents recorded in a mood description data set 22.
FIG. 6 is a diagram showing an example of contents recorded in an action schedule data set 23.
FIG. 7 is a diagram showing an example of contents recorded in a sensor data set 3;
FIG. 8 is a diagram showing an example of contents recorded in an integrated behavior data set 7;
FIG. 9 is a diagram showing an example of contents recorded in a behavior rule set 8;
FIG. 10 is a view showing an example of contents recorded in a related data set 9;
FIG. 11 is a diagram showing an example of contents recorded in a related data set 9;
FIG. 12 is a diagram showing an example of contents recorded in a related data set 9.
FIG. 13 is a view showing an example of contents recorded in a related data set 9;
FIG. 14 is a view showing an example of contents recorded in a related data set 9;
FIG. 15 is a view showing an example of contents recorded in a related data set 9;
FIG. 16 is a diagram showing an example of contents recorded in a related data set 9;
FIG. 17 is a diagram showing an example of contents recorded in a behavioral advice set 10;
FIG. 18 is a diagram showing an example of contents recorded in a motion constraint condition rule set 12.
FIG. 19 is a flowchart showing a processing procedure of the behavior management support device according to the present invention.
FIG. 20 is a diagram showing an outline of an action description process using a data input interface C4 of a main body C1.
FIG. 21 is a diagram showing an outline of an action description process using a data input interface C4 of a main body C1.
FIG. 22 is a diagram showing an outline of an action description process using a data input interface C4 of a main unit C1.
FIG. 23 is a diagram showing an outline of an action description process using a data input interface C4 of the main body C1.
FIG. 24 is a diagram showing an outline of an action description process using a data input interface C4 of a main unit C1.
FIG. 25 is a diagram showing an outline of an action description process using the data input interface C4 of the main unit C1.
FIG. 26 is a diagram showing an outline of an action description process using a data input interface C4 of a main body C1.
FIG. 27 is a diagram showing an outline of an action description process using a data input interface C4 of a main body C1.
FIG. 28 is a diagram showing an outline of an action description process using a data input interface C4 of the main body C1.
FIG. 29 is a diagram showing an outline of an action description process using the data input interface C4 of the main body C1.
FIG. 30 is a diagram showing an outline of an action description process using a data input interface C4 of a main body C1.
FIG. 31 is a diagram schematically showing a process of generating an integrated behavior data set 7;
FIG. 32 is a view schematically showing a process of reorganizing the action schedule data set 23.
FIG. 33 is a schematic view showing an example of an action graph.
FIG. 34 is a functional block diagram showing a second embodiment of the system shown in FIG. 1;
FIG. 35 is a functional block diagram showing a third embodiment of the system shown in FIG. 1;
[Explanation of symbols]
C1: body, C2: server device, C3: sensor head, C5: sensor group, C4: data input interface, 1: personal attribute data set, 2: action-related data group, 3: sensor data set, 4: data input Section, 5: data acquisition section, 6: action data processing section, 7: integrated action data set, 8: action rule set, 9: related data set, 10: action advice set, 11: advice evaluation set, 12: motion constraint Condition rule set, 13: knowledge sharing unit, 14: automatic position detection unit, 21: behavior data set, 22: mood description data set, 23: behavior schedule data set, 61: integrated behavior data generation unit, 62: behavior rule generation Section, 63: action schedule reorganization section, 64: action advice generation section, 65: advice evaluation input section

Claims (15)

In the behavior management support device for managing the behavior of the user using the biological information of the user detected by the sensor worn on the body of the user,
Integrated behavior database generating means for generating an integrated behavior database in which the biological information detected by the sensor and behavior-related information related to the behavior of the user are associated with each other in a time-series manner,
Behavior rule generation means for generating the user's behavior rules from the integrated behavior database generated by the integrated behavior database generation means,
Message generating means for generating a message for urging the user to exercise based on the user's action rules generated by the action rule generating means;
A behavior management support device comprising: a message notification unit that notifies the user of a message generated by the message generation unit. Further, action schedule acquisition means for acquiring a future action schedule of the user prepared in advance based on the user's will,
Based on the behavior rules of the user generated by the behavior rule generation means, comprising a behavior schedule reorganization means for reorganizing the behavior schedule to encourage the user to exercise,
The behavior management support device according to claim 1, wherein the message generation unit generates a message based on the action schedule reorganized by the behavior schedule reorganization unit. Further, mood description data acquisition means for acquiring mood description data that describes the mood of the user resulting from the action of the user via an input operation of the user,
The integrated behavior database generating means, in the integrated behavior database, correlate the biological information, the behavior-related information, and the mood description data with each other,
2. The behavior management support device according to claim 1, wherein the message generation unit generates the message in consideration of the mood description data in the integrated behavior database. Further, evaluation obtaining means for obtaining an evaluation of the user for the message notified by the message notifying means,
Constraint condition database generating means for generating a constraint condition database in which the evaluation obtained by the evaluation obtaining means and a message corresponding to the evaluation are associated with each other to form a database,
2. The behavior management support device according to claim 1, wherein the message generation unit generates the message in consideration of the constraint database generated by the constraint database generation unit. 3. The behavior management support device according to claim 4, further comprising a database sharing unit that shares the integrated behavior database and the constraint condition database among a plurality of users. Further, a position information acquisition unit for acquiring the position information of the user,
2. The behavior management support device according to claim 1, wherein the integrated behavior database generation unit associates the biological information, the behavior-related information, and the position information with each other in the integrated behavior database. In the behavior management support method used in the behavior management support device for managing the behavior of the user using the biological information of the user detected by the sensor worn on the body of the user,
An integrated behavior database generation step of generating an integrated behavior database in which biological information detected by the sensor and behavior-related information related to the behavior of the user are associated with each other in a time-series manner,
An action rule generation step of generating an action rule of the user from the integrated action database generated by the integrated action database generation step;
A message generation step of generating a message for prompting the user to exercise based on the user's behavior rule generated by the behavior rule generation step;
A message notification step of notifying the user of a message generated by the message generation step. Further, an action schedule obtaining step of obtaining a future action schedule of the user prepared in advance based on the user's will;
Based on the behavior rules of the user generated by the behavior rule generation step, comprising a behavior schedule reorganization step of reorganizing the behavior plan to encourage the user to exercise,
The action management support device according to claim 7, wherein the message generating step is a step of generating a message based on the action schedule reorganized by the action schedule reorganizing step. Further, a mood description data acquisition step of acquiring mood description data describing the mood of the user resulting from the action of the user through an input operation of the user,
The integrated behavior database generation step is a step of associating the biological information, the behavior-related information, and the mood description data with each other in the integrated behavior database,
8. The behavior management support method according to claim 7, wherein the message generation step is a step of generating the message in consideration of the mood description data in the integrated behavior database. Further, an evaluation obtaining step of obtaining an evaluation of the user for the message notified by the message notification step,
A constraint condition database generating step of generating a constraint condition database in which a database corresponding to the evaluation obtained by the evaluation obtaining step and the message corresponding to the evaluation are created,
8. The behavior management support method according to claim 7, wherein the message generating step is a step of generating the message in consideration of the constraint database generated by the constraint database generating step. Further, a position information acquisition step of acquiring position information of the user,
The behavior management support method according to claim 7, wherein the integrated behavior database generation step is a step of associating the biological information, the behavior-related information, and the position information with each other in the integrated behavior database. An activity management support program for causing a computer to execute a process for managing the behavior of the user using biological information of the user detected by a sensor attached to the body of the user,
To the computer,
Instructions for executing an integrated action database generating step of generating an integrated action database that associates the biological information detected by the sensor and the action-related information related to the action of the user in a time-series manner,
An instruction for executing an action rule generation step of generating an action rule of the user from the integrated action database generated by the integrated action database generation step;
An instruction for executing a message generation step of generating a message for prompting the user to exercise based on the user's behavior rule generated by the behavior rule generation step;
An instruction for executing a message notification step of notifying the user of a message generated by the message generation step. Further, an instruction for executing an action schedule obtaining step of obtaining a future action schedule of the user prepared in advance based on the user's will;
Based on the behavior rule of the user generated by the behavior rule generation step, including an instruction to execute a behavior schedule reorganization step of reorganizing the behavior schedule to encourage the user to exercise,
13. The computer-readable storage medium according to claim 12, wherein the message generating step is a step of generating a message based on the action schedule reorganized by the action schedule reorganizing step. Further, a mood description data acquisition step of acquiring mood description data describing the mood of the user resulting from the action of the user via an input operation of the user,
The integrated behavior database generation step is a step of associating the biological information, the behavior-related information, and the mood description data with each other in the integrated behavior database,
13. The behavior management support program according to claim 12, wherein the message generation step is a step of generating the message in consideration of the mood description data in the integrated behavior database. Further, an instruction for executing an evaluation obtaining step of obtaining an evaluation of the user for the message notified by the message notifying step,
An instruction for executing a constraint condition database generation step of generating a constraint condition database in which a database corresponding to the evaluation obtained by the evaluation obtaining step and the message corresponding to the evaluation is generated,
13. The behavior management support program according to claim 12, wherein the message generating step is a step of generating the message in consideration of the constraint database generated in the constraint database generating step.

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